Suchergebnisse - "self‐supervised algorithm"

A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images von Huang, Wenjian, Li, Hao, Wang, Rui, Zhang, Xiaodong, Wang, Xiaoying, Zhang, Jue

“… Purpose An automated accurate segmentation for dynamic contrast‐enhanced magnetic resonance (DCE‐MR) image sequences is essential for quantification of renal …”
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Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer’s Disease Using Voice von Agbavor, Felix, Liang, Hualou

“… At the core of our system is the pre-trained data2vec model, the first high-performance self-supervised algorithm that works for speech, vision, and text …”
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Self-supervised learning based knowledge distillation framework for automatic speech recognition for hearing impaired von Kumar, L. Ashok, Renuka, D. Karthika, Shunmuga, Priya M C, Madhumitha, G, Priyanka, S, Sangeeth, M, Subhiksha, R

“… We investigated on self-supervised algorithm for the unlabelled data. In recent years, these algorithms have progressed significantly, with their efficacy approaching …”
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A simple self-supervised learning framework with patch-based data augmentation in diagnosis of Alzheimer’s disease von Gong, Haoqiang, Wang, Zhiwen, Huang, Shuaihui, Wang, Jinfeng

“… and high cost of manual labeling in medical image processing. However, the existing self-supervised algorithms applied to medical images often have poor diagnostic effects and consume large resources, as a consequence …”
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Efficient Learning for Sensor Time Series in Label Scarce Applications von Chowdhury, Ranak Roy

“… Sensors in daily life span applications from healthcare and climate modeling to home automation and robotics. These sensors generate abundant time series data, …”
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Metaverse in the Wild: Modeling, Adapting, and Rendering of 3D Human Avatars from a Single Camera von Yoon, Jae Shin

“… Previous works have utilized a system of multiview cameras to generate the 3D avatars, which enables measuring appearance and 3D geometry of a subject …”
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